To tackle this problem, we first need to translate the given natural language description into a symbolic representation that can be used to formulate an optimization model. The variables in question are 'slices of pizza' and 'cherry pies', which will be represented symbolically as `x0` and `x1`, respectively.

The objective function aims to minimize the total cost, described as 6.19 times the quantity of slices of pizza plus 4.18 multiplied by the number of cherry pies. This translates to:

\[ \text{Minimize: } 6.19x_0 + 4.18x_1 \]

The constraints are as follows:
- Fiber constraint: \(5x_0 + 2x_1 \geq 17\)
- Fiber upper bound constraint: \(5x_0 + 2x_1 \leq 44\)
- Cost lower bound constraint: \(6x_0 + 6x_1 \geq 36\)
- Cost upper bound constraint: \(6x_0 + 6x_1 \leq 71\)
- Carbohydrates lower bound constraint: \(6x_0 + x_1 \geq 9\)
- Carbohydrates upper bound constraint: \(6x_0 + x_1 \leq 29\)
- Tastiness rating lower bound constraint: \(5x_0 + 2x_1 \geq 25\)
- Tastiness rating upper bound constraint: \(5x_0 + 2x_1 \leq 62\)
- Additional linear constraint: \(-8x_0 + 4x_1 \geq 0\)

Given the problem's requirements, we can represent it symbolically as follows:

```json
{
    'sym_variables': [('x0', 'slices of pizza'), ('x1', 'cherry pies')],
    'objective_function': '6.19*x0 + 4.18*x1',
    'constraints': [
        '5*x0 + 2*x1 >= 17', 
        '5*x0 + 2*x1 <= 44', 
        '6*x0 + 6*x1 >= 36', 
        '6*x0 + 6*x1 <= 71', 
        '6*x0 + x1 >= 9', 
        '6*x0 + x1 <= 29', 
        '5*x0 + 2*x1 >= 25', 
        '5*x0 + 2*x1 <= 62',
        '-8*x0 + 4*x1 >= 0'
    ]
}
```

Now, let's implement this optimization problem using Gurobi in Python:

```python
from gurobipy import *

# Create a new model
model = Model("Optimization_Problem")

# Add variables to the model
x0 = model.addVar(vtype=GRB.CONTINUOUS, name="slices_of_pizza")
x1 = model.addVar(vtype=GRB.CONTINUOUS, name="cherry_pies")

# Set the objective function
model.setObjective(6.19*x0 + 4.18*x1, GRB.MINIMIZE)

# Add constraints
model.addConstr(5*x0 + 2*x1 >= 17, "fiber_lower_bound")
model.addConstr(5*x0 + 2*x1 <= 44, "fiber_upper_bound")
model.addConstr(6*x0 + 6*x1 >= 36, "cost_lower_bound")
model.addConstr(6*x0 + 6*x1 <= 71, "cost_upper_bound")
model.addConstr(6*x0 + x1 >= 9, "carbohydrates_lower_bound")
model.addConstr(6*x0 + x1 <= 29, "carbohydrates_upper_bound")
model.addConstr(5*x0 + 2*x1 >= 25, "tastiness_rating_lower_bound")
model.addConstr(5*x0 + 2*x1 <= 62, "tastiness_rating_upper_bound")
model.addConstr(-8*x0 + 4*x1 >= 0, "additional_constraint")

# Optimize the model
model.optimize()

# Print the results
if model.status == GRB.OPTIMAL:
    print("Optimal solution found.")
    print("slices_of_pizza:", x0.x)
    print("cherry_pies:", x1.x)
    print("Objective function value:", model.objVal)
else:
    print("No optimal solution found.")
```